Modeling of genetic regulation networks based on Immune Memory Particle Swarm Optimization

A reverse-problem in modern systems biology focuses on modeling and simulation of gene regulatory networks, metabolic networks from time-course data. With the depth of research, not only the network structures but also its dynamics are paid more attention. We proposed a method to model the gene regulatory networks in Escherichia coli by using Immune Memory Particle Swarm Optimization(IM-PSO) and Ordinary differential equations (ODEs). This method successfully inferred the dynamics of a small regulation networks for 7 genes using only 4 time-course data of gene expression.